Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors

• URL: http://arxiv.org/abs/2512.04808v1
• Date: Thu, 04 Dec 2025 14:00:32 GMT
• Title: Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
• Authors: Puria Radmard, Paul M. Bays, Máté Lengyel,
• Abstract summary: We use a non-parametric generative model of behavioural responses to produce surrogate data for training RNNs.<n>To capture all relevant statistical aspects of the data, we developed a novel diffusion model-based approach for training RNNs.
• Score: 7.041349097212527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering the neural mechanisms underpinning cognition is one of the grand challenges of neuroscience. However, previous approaches for building models of RNN dynamics that explain behaviour required iterative refinement of architectures and/or optimisation objectives, resulting in a piecemeal, and mostly heuristic, human-in-the-loop process. Here, we offer an alternative approach that automates the discovery of viable RNN mechanisms by explicitly training RNNs to reproduce behaviour, including the same characteristic errors and suboptimalities, that humans and animals produce in a cognitive task. Achieving this required two main innovations. First, as the amount of behavioural data that can be collected in experiments is often too limited to train RNNs, we use a non-parametric generative model of behavioural responses to produce surrogate data for training RNNs. Second, to capture all relevant statistical aspects of the data, we developed a novel diffusion model-based approach for training RNNs. To showcase the potential of our approach, we chose a visual working memory task as our test-bed, as behaviour in this task is well known to produce response distributions that are patently multimodal (due to swap errors). The resulting network dynamics correctly qualitative features of macaque neural data. Importantly, these results were not possible to obtain with more traditional approaches, i.e., when only a limited set of behavioural signatures (rather than the full richness of behavioural response distributions) were fitted, or when RNNs were trained for task optimality (instead of reproducing behaviour). Our approach also yields novel predictions about the mechanism of swap errors, which can be readily tested in experiments. These results suggest that fitting RNNs to rich patterns of behaviour provides a powerful way to automatically discover mechanisms of important cognitive functions.

Related papers

• General Self-Prediction Enhancement for Spiking Neurons [71.01912385372577]
  Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
  arXiv  Detail & Related papers  (2026-01-29T15:08:48Z)
• Discovering alternative solutions beyond the simplicity bias in recurrent neural networks [36.12962884836429]
  Training recurrent neural networks (RNNs) to perform neuroscience-style tasks has become a popular way to generate hypotheses for how neural circuits might perform computations.<n>Recent work has demonstrated that task-trained RNNs possess a strong simplicity bias.<n>We propose Iterative Neural Similarity Deflation to break this inductive bias.
  arXiv  Detail & Related papers  (2025-09-25T19:59:04Z)
• Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks [3.049887057143419]
  Task-trained recurrent neural networks (RNNs) are widely used in neuroscience and machine learning to model computations.<n>Different RNNs trained on the same task and achieving similar performance can exhibit strikingly different internal solutions known as solution degeneracy.<n>Here, we develop a unified framework to quantify and control solution degeneracy across three levels: behavior, neural dynamics, and weight space.
  arXiv  Detail & Related papers  (2024-10-04T23:23:55Z)
• BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation [6.3559178227943764]
  We propose BLEND, a behavior-guided neural population dynamics modeling framework via privileged knowledge distillation.<n>By considering behavior as privileged information, we train a teacher model that takes both behavior observations (privileged features) and neural activities (regular features) as inputs.<n>A student model is then distilled using only neural activity.
  arXiv  Detail & Related papers  (2024-10-02T12:45:59Z)
• Inferring stochastic low-rank recurrent neural networks from neural data [5.179844449042386]
  A central aim in computational neuroscience is to relate the activity of large neurons to an underlying dynamical system.<n>Low-rank recurrent neural networks (RNNs) exhibit such interpretability by having tractable dynamics.<n>Here, we propose to fit low-rank RNNs with variational sequential Monte Carlo methods.
  arXiv  Detail & Related papers  (2024-06-24T15:57:49Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Path classification by stochastic linear recurrent neural networks [2.5499055723658097]
  We show that RNNs retain a partial signature of the paths they are fed as the unique information exploited for training and classification tasks. We argue that these RNNs are easy to train and robust and back these observations with numerical experiments on both synthetic and real data.
  arXiv  Detail & Related papers  (2021-08-06T12:59:12Z)
• Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment [57.91534223695695]
  We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in rats. Our method demonstrates how the dynamic activity in evolved RNNs can capture interesting and complex cognitive behavior.
  arXiv  Detail & Related papers  (2021-02-25T02:13:52Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data [77.92736596690297]
  We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
  arXiv  Detail & Related papers  (2020-05-05T14:16:54Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.